The present invention relates to fine pattern examining methods and apparatus using detailed shape or size measurement based on non-destructive observation and image processing, with a scanning microscope and methods of evaluating the examining apparatus.
In semiconductor and other industries, evaluation of fine roughness of randomly occurring pattern edges called edge roughness has been required as the pattern processing sizes become finer. Especially, in semiconductor process it has been known that a local fluctuation of a line width or line width roughness occurring from a (line) edge roughness or a line's right and left edge roughness of a gate or interconnect pattern will greatly influence the device performance. Thus, even in the pattern shape evaluation of the semiconductor process, the line-edge or line width roughness need be measured with high accuracy.
In order to measure the line-edge or line width roughness, however, a set of points representing a pattern edge need be obtained from an observation image displayed on a scanning electron microscope. Random noise occurring in the image acquirement will greatly influence this work. As will be described later in more detail, the influence of the random noise will appear as a bias quantity of the roughness index. A measured value of the roughness will be greater than that obtained from the shape of a real observation pattern.
The bias quantity has been a question in recent years under the following circumstances although it does not come into question when the real roughness value is much greater than the bias quantity. First, damage to a specimen or changes in the dimensions of the specimen (due to pattern shrinkage, charging, adhesion of organic molecules, etc.) due to irradiation of the specimen with an electron beam can be a problem. In order to prevent this damage or size changes, the observation should be made with as small a dose of electron beam irradiation as possible. If the dose of electron beam irradiation is less, however, the ratio in strength of signal to noise (S/N ratio) becomes smaller. Second, there are demands for observation of only higher frequency components of the roughness. When the higher frequency components of the roughness are observed, or a high frequency roughness or short-period roughness is observed, the roughness will be measured on a short line. Then, since no longer-period components are measured, the roughness will be measured on a short line. As a result, the roughness value itself will be smaller. In contrast, since the roughness bias quantity due to noise is a fluctuation quantity per edge point, the bias quantity will not become smaller even when the measured line length is reduced. In other words, if the roughness in the high frequency area is intended to be measured, the bias due to noise will increase relatively. Under these circumstances, it is necessary to eliminate the influence of random noise from the obtained measured value, and calculate a value of the real roughness of the observation pattern.
Simultaneously, there are demands for quantifying noise itself for the purpose of evaluating the performance of the measuring instrument.
In summary, it is necessary to separately take a quantity of roughness and the influence of noise actually present in the observation pattern present in the observation pattern from the index of the edge roughness observed usually.
In the following, a method of generally calculating the index of line-edge or line width roughness, the influence of random noise on the value of the calculated index value, and a conventional method of separating the index value and the random noise will be described.
The edge of a line pattern is calculated as follows. First, the pattern is observed from above with the scanning electron microscope. Let y direction be the direction of a line in a two-dimensional signal intensity distribution obtained and let x direction be the direction perpendicular to the direction of the line. An x-direction distribution of the signal intensity with y as a constant is referred to as a profile of the signal intensity. Such profiles are arranged at constant intervals in the y-direction. When a y-coordinate is specified, a corresponding profile is determined uniquely. FIG. 1 shows a relationship in correspondence between profile and actual pattern cross-sectional view. An upper sub-view of FIG. 1 illustrates a profile actually obtained and a lower part of FIG. 1 shows a cross-sectional view of a line pattern corresponding to the profile. The edge of a line pattern corresponds to a peak of the profile. When an edge roughness is analyzed, edge points are defined according to a given algorism on the profile obtained by actual measurement. Thus, the edge point defined according to the algorism can not necessarily coincide with a peak appearing on the profile. When a value is specified (and referred to as i) on the y-coordinate is specified, an x-coordinate of a point corresponding to the edge can be calculated for the corresponding profile. Then, y-coordinates can be specified at constant intervals and then pattern edges can be extracted one after another from the corresponding profiles, thereby obtaining series data of the pattern edge. FIG. 2 schematically illustrates an enlarged view of a part of a line pattern observed on a SEM image. FIG. 2 illustrates obtaining series data Δxi; Δx1, Δx2, . . . each indicative of a difference between a straight line approximating series data of an edge and a respective one of actual pattern edge positions. The approximate straight-line comprises a set of averaged values of the edge points, and Δxi corresponds to a deviation of an edge point in a specified profile from the averaged value. FIG. 3 comprises a FIG. 2 viewed in perspective. The width of the line pattern is represented by an interval between right and left approximate straight-lines. A (local) line width on a specified profile is represented by a series difference wi (=w1, w2, . . . ) between the right and left edge series.
Indexes representing degrees of line-edge roughness and line width roughness will be described together as a roughness index in the following. As roughness indexes, (Δx1, Δx2, . . . ) or (w1, w2, . . . ) is regarded as a set of data and a standard deviation obtained from these data values or three times the standard deviation is generally used. Even at present, these indexes are used in the resist materials and in the screening of a process. In addition, in the future, it is considered that even in the dimension check of the mass production process not only the conventional simple averaged line width or the line's CD (Critical Dimension) but also the roughness indexes need be checked. At this time, an index of the line-edge or line width roughness need be calculated with high accuracy. As shown by non-patent literature 1 shown later, the performance of a transistor is predictable from the values of the indexes of the line width roughness, but also in this case, a high-accuracy width roughness need be obtained.
Proc. SPIE 5375 (2000), pp 468–476 discloses a technique for setting measurement parameters comprising an extent of a measurement area in the roughness measurement and that of an interval at which the edge point is sampled, based on a spatial frequency distribution of roughness. In addition to these measurement parameters, the device performance and more particularly an extent of noise influence the measured values of the actual roughness indexes. Proc. SPIE 5375 (2004), pp 515–533 shown later discloses a view that the positions of the edge points which will be observed on each profile have a distribution round a real edge point. Such distribution is considered as arising from noise. Let σe be a distribution (or standard deviation) of the observed edge point positions around the actual position. Then, an edge roughness index σm observed is given byσm=√{square root over (σ02+σe2)}  Ex. 1where σ0 is the actual edge roughness index (represented by a standard deviation).
That is, the observed value of the edge roughness index is larger than the real value. In the present description text, a change of the edge roughness index from its real value is referred to as a bias of the edge roughness. Occurrence of the bias is mainly due to noise.
Similarly, this applies when an object is not edge roughness, but line width roughness. In the case of the line width roughness, observed variation in the positions of the right and left edge points adds to that of the local line widths and the observed value of the line width roughness index is larger than the real value. In the following, the edge roughness will be mainly discussed.
Even when a bias is present in the observed value of the edge roughness, the edge roughness index still represents the feature of the pattern shape. When σ0 becomes smaller, σm becomes rather closer to σe, and does not represent an extent of the edge roughness correctly. When a pattern of small line-edge roughness is measured, it is necessary to eliminate the influence of the edge roughness bias and obtain as close a value to the real index value σ0 as possible. Even when an index (such as, for example, a deviation average) other than the above example (or standard deviation) is used as that of the line-edge or line width roughness, the observed value will likewise have a bias that reflects noise. In the present invention, standard deviation will be used as an index of roughness in every case for purposes of easy understanding.
Proc. SPIE 5375 (2004), pp 515–533 discloses a method of separating a real value σ0 and a term σe due to noise from a measured value σm of expression 1. In this method, an object pattern is observed a plurality of times, thereby obtaining a plurality of images corresponding to two-dimensional signal intensity distributions. Then, all these data are added up (more particularly, the two-dimensional intensity distributions are either added or averaged) and edge point positions are obtained which considered to be close to the real edge point positions (referred to as averaged edge points temporarily) from the obtained added-up data. The edge point positions obtained by observation from data for a plurality of pages of data are distributed around the averaged edge point. A standard deviation of this distribution is then calculated and represented by σe.
As described above, since the line-edge or line width roughness influences the characteristics of a semiconductor device, the value of the line-edge or line width roughness can be used as a criterion for determining whether the semiconductor manufacturing process is good or not. Thus, in order to evaluate the process, it is necessary to calculate a real roughness index value representing the line-edge or line width roughness minus a change quantity due to noise. A bias due to noise included in the line-edge or line width roughness involves reproducibility of evaluation of a semiconductor evaluation device, namely, a degree of a distribution of the measured values of parameters for the purpose of evaluation. Thus, in order to evaluate the semiconductor evaluation device, a distribution of the edge point positions itself due to noise need be evaluated.
The problem with the method disclosed in non-patent literature 2 is that first, the method is very time consuming. First, two sets or more of image data of the same visual field must be acquired. According to non-patent literature 2, the image data need be processed statistically, and at least two sets of image data is required. In addition, in order to obtain a result of high reliability, measurement must be made at least five positions empirically. Since the image data need be added in the same visual field, position deviations contained in the image data should be corrected. When image data is acquired using the scanning electron microscope, the position of an examination specimen can deviate from the visual field of the image due to thermal vibration of the specimen or a drift of the stage. Correction of the position deviation is time consuming and requires the operator's experience and data processing is complicated. In addition, automation is difficult. A second problem is to damage to the specimen. In order to acquire data on a plurality of images, it is necessary to irradiate the specimen repeatedly with an electron beam (EB). Even when a beam irradiation time per unit image pickup operation is short, the whole EB radiation quantity increases by repeated irradiation.
As described above, the conventional method of calculating a bias component included in the line-edge or line width roughness needs two sets or more of image data and takes a long time for data processing. In addition, data analysis that requires skill need be made as a preprocess for the image data processing. Furthermore, the beam irradiation time for the specimen increases, thereby damaging the observation pattern possibly.
The problem to be solved by the present invention is to provide a method and apparatus for evaluating an index of line-edge or line width roughness present actually in an object to be observed, and a roughness component due to noise contained in a result of the observation from a piece of image obtained in a usual pattern observation, in a shorter time than in the past without losing substantially the same accuracy as in the conventional method.